Semantic network theory of learning

Theory · All theories
Evidence · 6 claims (4 for, 2 mixed) · 7 studies (4 causal, 3 quant-synthesis), q1–q4 · 3 of 7 report an effect size · 5 claims rest on one study

Description

Knowledge is represented as semantic networks of nodes and links; learning consists of building new structures by constructing new nodes and interrelating them with existing nodes. The article uses this framework to define structural knowledge as knowledge of how concepts are interrelated, and frames learning as reorganization of the learner's knowledge structure to resemble the expert's, which mapping exercises are meant to induce.

Design Implications

Context

Requirements

• Measures such as proximity scores compared with experts' scores to assess structural knowledge

Constraints

Target Learners

• learners in structured domains such as introductory psychology

Target Learning Objectives

• reorganizing the learner's knowledge structure toward the expert's structure

Claims

• Structural Knowledge Similarity Predicts Exam Performance [+M]
• Structural Knowledge Mediates Declarative To Procedural [+W]
• Concept mapping improves learning over most comparison conditions by a moderate average amount, but not over time-matched retrieval practice [+M]
• ESL students taught concept mapping show significantly greater pretest-to-posttest achievement gains than students taught an alternative learning strategy [+W] — attached 2026-10-16 from Pasana Chularut (2001), which proposed "Concept mapping as a metacognitive learning strategy grounded in Ausubel's assimilation theory of meaningful learning".
• Concepts become more interrelated with practice under discriminate instructions, and more isolated with practice under image instructions [~W]
• Whether a prototype or caricature is categorized more easily is predicted to depend on concept interrelatedness: prototype advantage for isolated, caricature advantage for interrelated concepts [~W]
• Concept-mapping effect sizes vary by science subject (physics/earth science g = 1.040, biology g = 0.671, chemistry g = 0.590) but not significantly [+W]
• In Experiment 1, model concepts increase monotonically with expertise while detailed biomedical concepts show an inverted U, confirming knowledge encapsulation [+W] — attached 2026-10-23 from Boshuizen et al. (1995), which proposed "Theory of medical expertise development through knowledge encapsulation and illness script formation"; tests this page's relationship.

Related Theories

• Semantic Mapping Principle
• Connectivism

Examples

• Concept/semantic map
• Student-generated mapping exercise
• Teacher-provided graphic map as review

Key Sources

• Jonassen, D. H. (1984). Semantic relationships and structural knowledge acquisition in introductory psychology. https://eric.ed.gov/?id=ED258538

Page metadata

Field	Value
type	theory
title	Semantic network theory of learning
description	Knowledge is represented as semantic networks of nodes and links; learning consists of building new structures by constructing new nodes and interrelating them with existing nodes.
status	review
canonical	True
generated	by claude/unspecified on 2026-09-25
trust	unverified — no one has confirmed this page's content
sources	1 — jonassen-1984

Frontmatter is the page's machine-readable record — see the Schema & Guide.